Publications

Towards AI-designed genomes using a variational autoencoder
N.K. Dudek
Doina Precup
Synthetic biology holds great promise for bioengineering applications such as environmental bioremediation, probiotic formulation, and produ… (see more)ction of renewable biofuels. Humans’ capacity to design biological systems from scratch is limited by their sheer size and complexity. We introduce a framework for training a machine learning model to learn the basic genetic principles underlying the gene composition of bacterial genomes. Our variational autoencoder model, DeepGenomeVector, was trained to take as input corrupted bacterial genetic blueprints (i.e. complete gene sets, henceforth ‘genome vectors’) in which most genes had been “removed”, and re-create the original. The resulting model effectively captures the complex dependencies in genomic networks, as evaluated by both qualitative and quantitative metrics. An in-depth functional analysis of a generated gene vector shows that its encoded pathways are interconnected and nearly complete. On the test set, where the model’s ability to re-generate the original, uncorrupted genome vector was evaluated, an AUC score of 0.98 and an F1 score of 0.82 provide support for the model’s ability to generate diverse, high-quality genome vectors. This work showcases the power of machine learning approaches for synthetic biology and highlights the possibility that just as humans can design an AI that animates a robot, AIs may one day be able to design a genomic blueprint that animates a carbon-based cell. SIGNIFICANCE STATEMENT Genomes serve as the blueprints for life, encoding complex networks of genes whose products must seamlessly interact to result in living organisms. In this work, we develop a framework for training a machine learning algorithm to learn the basic genetic principles that underlie genome composition. This innovation may eventually lead to improvements in the genome design process, increasing the speed and reliability of designs while decreasing cost. It further suggests that AI agents may one day have the potential to design blueprints for carbon-based life.
2023-10-22
bioRxiv (preprint)
doi.org
Unsupervised Improvement of Audio-Text Cross-Modal Representations
Zhepei Wang
Cem (Yusuf) Subakan
Krishna Subramani
Junkai Wu
Tiago Tavares
Fabio Ayres
Paris Smaragdis
Recent advances in using language models to obtain cross-modal audio-text representations have overcome the limitations of conventional trai… (see more)ning approaches that use predefined labels. This has allowed the community to make progress in tasks like zero-shot classification, which would otherwise not be possible. However, learning such representations requires a large amount of human-annotated audio-text pairs. In this paper, we study unsupervised approaches to improve the learning framework of such representations with unpaired text and audio. We explore domain-unspecific and domain-specific curation methods to create audio-text pairs that we use to further improve the model. We also show that when domain-specific curation is used in conjunction with a soft-labeled contrastive loss, we are able to obtain significant improvement in terms of zero-shot classification performance on downstream sound event classification or acoustic scene classification tasks.
2023-10-22
2023 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) (published)
doi.org
arxiv.org
A Novel Information-Theoretic Objective to Disentangle Representations for Fair Classification
Pierre Colombo
Nathan Noiry
Guillaume Staerman
Pablo Piantanida
2023-10-21
ArXiv (preprint)
doi.org
arxiv.org
ConceptGraphs: Open-Vocabulary 3D Scene Graphs for Perception and Planning
Qiao Gu
Alihusein Kuwajerwala
Sacha Morin
Krishna Murthy
Bipasha Sen
Aditya Agarwal
Corban Rivera
William Paul
Kirsty Ellis
Rama Chellappa
Chuang Gan
Celso M de Melo
Joshua B. Tenenbaum
Antonio Torralba
Florian Shkurti
Liam Paull
For robots to perform a wide variety of tasks, they require a 3D representation of the world that is semantically rich, yet compact and effi… (see more)cient for task-driven perception and planning. Recent approaches have attempted to leverage features from large vision-language models to encode semantics in 3D representations. However, these approaches tend to produce maps with per-point feature vectors, which do not scale well in larger environments, nor do they contain semantic spatial relationships between entities in the environment, which are useful for downstream planning. In this work, we propose ConceptGraphs, an open-vocabulary graph-structured representation for 3D scenes. ConceptGraphs is built by leveraging 2D foundation models and fusing their output to 3D by multi-view association. The resulting representations generalize to novel semantic classes, without the need to collect large 3D datasets or finetune models. We demonstrate the utility of this representation through a number of downstream planning tasks that are specified through abstract (language) prompts and require complex reasoning over spatial and semantic concepts. (Project page: https://concept-graphs.github.io/ Explainer video: https://youtu.be/mRhNkQwRYnc )
2023-10-20
robot-learning.org/CoRL/2023/Workshop/LangRob (poster)
doi.org
openreview.net
Fundamental Limits of Membership Inference Attacks on Machine Learning Models
Eric Aubinais
Elisabeth Gassiat
Pablo Piantanida
Membership inference attacks (MIA) can reveal whether a particular data point was part of the training dataset, potentially exposing sensiti… (see more)ve information about individuals. This article provides theoretical guarantees by exploring the fundamental statistical limitations associated with MIAs on machine learning models. More precisely, we first derive the statistical quantity that governs the effectiveness and success of such attacks. We then deduce that in a very general regression setting with overfitting algorithms, attacks may have a high probability of success. Finally, we investigate several situations for which we provide bounds on this quantity of interest. Our results enable us to deduce the accuracy of potential attacks based on the number of samples and other structural parameters of learning models. In certain instances, these parameters can be directly estimated from the dataset.
2023-10-20
ArXiv (preprint)
doi.org
arxiv.org
Surprise-Adaptive Intrinsic Motivation for Unsupervised Reinforcement Learning
Adriana Hugessen
Roger Creus Castanyer
Glen Berseth
2023-10-20
NeurIPS.cc/2023/Workshop/IMOL (oral)
openreview.net
openreview.net
Audio Editing with Non-Rigid Text Prompts
Francesco Paissan
Zhepei Wang
Mirco Ravanelli
Paris Smaragdis
Cem (Yusuf) Subakan
In this paper, we explore audio-editing with non-rigid text edits. We show that the proposed editing pipeline is able to create audio edits … (see more)that remain faithful to the input audio. We explore text prompts that perform addition, style transfer, and in-painting. We quantitatively and qualitatively show that the edits are able to obtain results which outperform Audio-LDM, a recently released text-prompted audio generation model. Qualitative inspection of the results points out that the edits given by our approach remain more faithful to the input audio in terms of keeping the original onsets and offsets of the audio events.
2023-10-19
ArXiv (preprint)
doi.org
arxiv.org
Detection and evaluation of bias-inducing features in machine learning
Moses Openja
gabriel laberge
Foutse Khomh
2023-10-19
ArXiv (preprint)
doi.org
arxiv.org
Graph embedding and transfer learning can help predict potential species interaction networks despite data limitations
Tanya Strydom
Salomé Bouskila
Francis Banville
Ceres Barros
Dominique Caron
Maxwell J. Farrell
Marie‐Josée Fortin
Benjamin Mercier
Laura J. Pollock
Rogini Runghen
Giulio V. Dalla Riva
Timothée Poisot
Metawebs (networks of potential interactions within a species pool) are a powerful abstraction to understand how large‐scale species inter… (see more)action networks are structured. Because metawebs are typically expressed at large spatial and taxonomic scales, assembling them is a tedious and costly process; predictive methods can help circumvent the limitations in data deficiencies, by providing a first approximation of metawebs. One way to improve our ability to predict metawebs is to maximize available information by using graph embeddings, as opposed to an exhaustive list of species interactions. Graph embedding is an emerging field in machine learning that holds great potential for ecological problems. Here, we outline how the challenges associated with inferring metawebs line‐up with the advantages of graph embeddings; followed by a discussion as to how the choice of the species pool has consequences on the reconstructed network, specifically as to the role of human‐made (or arbitrarily assigned) boundaries and how these may influence ecological hypotheses.
2023-10-19
Methods in Ecology and Evolution (published)
doi.org
Mean-field games among teams
Jayakumar Subramanian
Akshat Kumar
Aditya Mahajan
2023-10-18
ArXiv (preprint)
doi.org
arxiv.org
Open-Set Multivariate Time-Series Anomaly Detection
Thomas Lai
Thi Kieu Khanh Ho
Narges Armanfard
2023-10-18
ArXiv (preprint)
doi.org
arxiv.org
A Persuasive Approach to Combating Misinformation
Safwan Hossain
Andjela Mladenovic
Yiling Chen
Gauthier Gidel
Bayesian Persuasion is proposed as a tool for social media platforms to combat the spread of misinformation. Since platforms can use machine… (see more) learning to predict the popularity and misinformation features of to-be-shared posts, and users are largely motivated to share popular content, platforms can strategically signal this informational advantage to change user beliefs and persuade them not to share misinformation. We characterize the optimal signaling scheme with imperfect predictions as a linear program and give sufficient and necessary conditions on the classifier to ensure optimal platform utility is non-decreasing and continuous. Next, this interaction is considered under a performative model, wherein platform intervention affects the user's future behaviour. The convergence and stability of optimal signaling under this performative process are fully characterized. Lastly, we experimentally validate that our approach significantly reduces misinformation in both the single round and performative setting and discuss the broader scope of using information design to combat misinformation.
2023-10-18
ArXiv (preprint)
doi.org
arxiv.org